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Cost‐penalized estimation and prediction evaluation for split‐plot designs
Author(s) -
Liang Li,
AndersonCook Christine M.,
Robinson Timothy J.
Publication year - 2007
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.835
Subject(s) - restricted randomization , split plot , factorial experiment , factorial , plot (graphics) , design of experiments , statistics , computer science , fractional factorial design , mathematics , mathematical optimization , randomization , medicine , randomized block design , mathematical analysis , surgery , randomized controlled trial
Comparisons between different designs have traditionally focused on balancing the quality of estimation or prediction relative to the overall size of the design. For split‐plot designs with two levels of randomization, the total number of observations may not accurately summarize the true cost of the experiment, because different costs are likely associated with setting up the whole and subplot levels. In this paper, we present several flexible measures for design assessment based on D ‐, G ‐ and V ‐optimality criteria that take into account potentially different cost structures for the split‐plot designs. The new measures are illustrated with two examples: a 2 3 factorial experiment for first‐order models, where all possible designs are considered, and selective designs for a three‐factor second‐order model. Copyright © 2006 John Wiley & Sons, Ltd.